Industrial legacy sites present persistent environmental and public health challenges due to heterogeneous contamination, fragmented historical records, and competing remediation priorities. Decision-makers often face difficulties in allocating limited resources effectively across complex spatial contexts characterized by uncertainty, multiple stakeholders, and interacting risk pathways. This study proposes a Geospatial Decision-Support System (GDSS) designed to prioritize environmental interventions in complex industrial legacy sites by integrating spatial analytics, multi-criteria decision analysis, and risk-based assessment within a unified computational framework. The proposed system synthesizes geospatial datasets including land use history, contaminant distribution, hydrogeological conditions, population vulnerability, ecological receptors, and infrastructure constraints to generate transparent, evidence-based prioritization outputs. A modular architecture is adopted, enabling flexible data ingestion, scenario testing, and iterative updating as new information becomes available. Spatial weighting and normalization techniques are combined with expert-informed criteria scoring to capture both quantitative indicators and contextual knowledge that are often excluded from conventional remediation planning tools. The GDSS incorporates uncertainty handling through sensitivity analysis and probabilistic overlays, allowing users to examine how data gaps and assumption variability influence intervention rankings. Visualization dashboards translate complex analytical results into intuitive maps and decision matrices that support stakeholder communication and regulatory engagement. The framework is particularly suited to data-limited environments where monitoring coverage is uneven and historical contamination patterns are poorly constrained. By explicitly linking spatial risk profiles to intervention feasibility and potential impact, the system supports strategic sequencing of remediation actions, optimization of monitoring investments, and alignment with sustainability and land-reuse objectives. A conceptual application demonstrates how the GDSS can differentiate high-priority zones requiring immediate intervention from areas suitable for long-term management or redevelopment planning. The study contributes a scalable and adaptable decision-support approach that enhances transparency, consistency, and accountability in environmental management of industrial legacy sites. Overall, the proposed GDSS advances geospatially informed environmental governance by enabling more rational, defensible, and impact-oriented prioritization of interventions under conditions of complexity and uncertainty. Its design supports integration with policy frameworks, community engagement processes, and adaptive management strategies, ensuring that remediation decisions remain responsive to evolving environmental conditions, regulatory expectations, and socio-economic development goals over extended project lifecycles. Globally applicable.